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Found 7 papers, 0 papers with code
Physics-informed reinforcement learning via probabilistic co-adjustment functions
Reinforcement learning of real-world tasks is very data inefficient, and extensive simulation-based modelling has become the dominant approach for training systems.
Towards AI-controlled FES-restoration of movements: Learning cycling stimulation pattern with reinforcement learning
By using just 100 seconds of cycling data, our method can deliver a fine-tuned pattern that gives better cycling performance.
Towards AI-controlled FES-restoration of arm movements: Controlling for progressive muscular fatigue with Gaussian state-space models
Yet, one remaining challenge of using RL to control FES is unobservable muscle fatigue that progressively changes as an unknown function of the stimulation, breaking the Markovian assumption of RL.
Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching
In combination, our customisable models and RL-based control method open the possibility of delivering customised FES controls for different subjects and settings with minimal engineering intervention.
Neuromuscular Reinforcement Learning to Actuate Human Limbs through FES
Functional Electrical Stimulation (FES) is a technique to evoke muscle contraction through low-energy electrical signals.
I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation
However, an open challenge remains on how to restore motor abilities to human limbs through FES, as the problem of controlling the stimulation is unclear.
Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling
Functional Electrical Stimulation (FES) can restore motion to a paralysed person's muscles.
